We’ll Fix it in Post: What Do Bug Fixes in Video Game Update Notes Tell Us?

Truelove, Andrew; Almeida, Eduardo Santana de; Ahmed, Iftekhar

doi:10.1109/icse-companion52605.2021.00120

Cited by 4 publications

(7 citation statements)

References 16 publications

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“…Three recent studies [33,40,46] suggest that finding performance issues in video games is a relevant problem, according to both game developers [40,46] and players [33]. Nevertheless, to the best of our knowledge, no previous work introduced automated approaches for load testing video games.…”

Section: Threats To Validitymentioning

confidence: 99%

“…Similarly, Naughty Dog used specialized profiling tools 1 for finding which parts of a given scene caused a drop in the number of frames per second (FPS) in The Last of Us. Truelove et al [46] report that game developers agree that Implementation response problems (among which, performancerelated ones) may severely impact the game experience. Also, Li et al [33] observed that players frequently complain about performance issues in game reviews.…”

Section: Introductionmentioning

confidence: 99%

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Using Reinforcement Learning for Load Testing of Video Games

Tufano¹,

Scalabrino²,

Pascarella³

et al. 2022

Preprint

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Different from what happens for most types of software systems, testing video games has largely remained a manual activity performed by human testers. This is mostly due to the continuous and intelligent user interaction video games require. Recently, reinforcement learning (RL) has been exploited to partially automate functional testing. RL enables training smart agents that can even achieve super-human performance in playing games, thus being suitable to explore them looking for bugs. We investigate the possibility of using RL for load testing video games. Indeed, the goal of game testing is not only to identify functional bugs, but also to examine the game's performance, such as its ability to avoid lags and keep a minimum number of frames per second (FPS) when highdemanding 3D scenes are shown on screen. We define a methodology employing RL to train an agent able to play the game as a human while also trying to identify areas of the game resulting in a drop of FPS. We demonstrate the feasibility of our approach on three games. Two of them are used as proof-of-concept, by injecting artificial performance bugs. The third one is an open-source 3D game that we load test using the trained agent showing its potential to identify areas of the game resulting in lower FPS.

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Section: Threats To Validitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using Reinforcement Learning for Load Testing of Video Games

Tufano¹,

Scalabrino²,

Pascarella³

et al. 2022

Preprint

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“…A recent taxonomy of issues in video games by Truelove et al [28] (which extends the one by Lewis et al [14]) reports 20 different kinds of issues. We will use such a taxonomy as a base to define the labels we want to assign to the video segments.…”

Section: Taxonomies Of Video Game Issuesmentioning

confidence: 99%

“…It is possible that we do not consider some relevant categories of issues. To mitigate this limitation, we relied on a state-of-the-art taxonomy [28].…”

Section: Limitations Challenges and Mitigationsmentioning

confidence: 99%

“…Because of the few automated approaches available for quality control in video game development [22], many games are released with unknown problems that are revealed only once customers start playing [28]. Since many streamers daily publish hours of gameplay videos, it is very likely that some of them experience such issues and leave traces of them in the uploaded videos.…”

Section: Introductionmentioning

confidence: 99%

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Towards Using Gameplay Videos for Detecting Issues in Video Games

Guglielmi¹,

Scalabrino²,

Bavota³

et al. 2022

Preprint

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Context. The game industry is increasingly growing in recent years. Every day, millions of people play video games, not only as a hobby, but also for professional competitions (e.g., e-sports or speedrunning) or for making business by entertaining others (e.g., streamers). The latter daily produce a large amount of gameplay videos in which they also comment live what they experience. Since no software and, thus, no video game is perfect, streamers may encounter several problems (such as bugs, glitches, or performance issues). However, it is unlikely that they explicitly report such issues to developers. The identified problems may negatively impact the user's gaming experience and, in turn, can harm the reputation of the game and of the producer. Objective. We aim at proposing and empirically evaluating GELID, an approach for automatically extracting relevant information from gameplay videos by (i) identifying video segments in which streamers experienced anomalies; (ii) categorizing them based on their type and context in which appear (e.g., bugs or glitches appearing in a specific level or scene of the game); and (iii) clustering segments that regard the same specific issue. Method. We will build on top of existing approaches able to identify videos that are relevant for a specific video game. These represent the input of GELID that processes them to achieve the defined objectives. We will experiment GELID on several gameplay videos to understand the extent to which each of its steps is effective. CCS CONCEPTS• Software and its engineering → Software evolution; Maintaining software; Software defect analysis.

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Improving Bug Reproduction through Game Engine State Analysis

Pinto Gomez,

Petrillo

2024

Proceedings of the ACM/IEEE 8th International Workshop on Games and Software Engineering

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We’ll Fix it in Post: What Do Bug Fixes in Video Game Update Notes Tell Us?

Cited by 4 publications

References 16 publications

Using Reinforcement Learning for Load Testing of Video Games

Using Reinforcement Learning for Load Testing of Video Games

Towards Using Gameplay Videos for Detecting Issues in Video Games

Improving Bug Reproduction through Game Engine State Analysis

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